Addressing the issues of insufficient object detection accuracy in UAV aerial images caused by factors such as high proportion of small targets, large scale differences among targets, and complex backgrounds, and considering the limited computational power and power consumption of edge devices, this paper proposes an improved object detection algorithm called EGD-YOLO based on YOLOv8n. First, a P2 layer for small target detection is added while the P5 layer for large target detection is removed, and the shallow channel expansion strategy is adopted to enhance the feature representation capability for small targets. Secondly, a global hierarchical fusion architecture cascading Multi-scale feature fusion and weighted feature fusion was designed to achieve efficient propagation and deep integration of cross-scale semantic information in the neck network. Finally, a DyHead dynamic detection head with multiple attention mechanisms is employed to further optimize the model′s small target detection performance. Experiments on the VisDrone2019 dataset demonstrate that the proposed EGD-YOLO achieves improvements of 12.0% in mAP@0.5 and 8.6% in mAP@0.5:0.95 over the baseline while maintaining a clear computational advantage; results on the DOTA dataset further confirm its strong generalization capability, providing an effective solution for small-object detection in UAV aerial imagery.